In recent years, applications of 3D measurement technologies are becoming increasingly widespread; these include dual-lens 3D cameras which measure the distance between an object and the camera based on differences in the angle of view (AOV) between the two lenses. Another example is the time-of-flight technology, which measures the time elapsed from emitting a beam of detection light (such as a laser beam) to receiving the returned signal for determining the distance to the reflecting surface of the object.
Structured-light scanning technology is another 3D measurement technology; scanning light patterns (spaced stripes) that are projected onto the object's surface from which the image of the scanned object is captured through the optical reception equipment and subsequently analyzed to obtain the 3D morphology of the object. In other words, surface irregularities often distort the shapes of projected stripes. Therefore, the 3D morphology of the object can be estimated from the distortion of the stripes.
However, although most of the existing 3D measurement technologies can detect the 3D morphology of sceneries, considerable computational resources are required from backend equipment in order to calculate the 3D data for each point of each object in the scenery; this increases computation time as well as hardware requirements, which lengthens processing time and raises equipment costs.
Therefore, how to provide effective 3D measurement methods and 3D measurement devices using the same, is an issue to be addressed in the field.